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This paper uses a set of routinely collected high-frequency data in low-income countries (LICs) to construct an aggregate and a comprehensive index of economic activity which could serve (i) as a measure of the direction of economic activity; and (ii) as a useful input in analyzing contemporaneous real sector performance in LICs in the absence of high-frequency, and often outdated, GDP data. It could also serve as a useful tool for policymakers to gauge short-term dynamics of economic activity and shape appropriate and timely policy responses

The first part of this paper is given over to a discussion of definitions, terminology, and objectives in income analysis. The second part deals with methods and sources of material in the study of cash income. The third part describes some results obtained to date in this work.
"Agricultural income" is defined as the value of goods and services produced on farms which become available during a given period for consumption or investment, after providing for the maintenance and replacement of goods and services employed in production. Since the national income is the final test of the state of economic activity, it is essential that the national income data be as complete and accurate as possible. The completeness and accuracy of the national income estimate are dependent upon the completeness and accuracy of the estimates for the various industries. The income from agriculture should, therefore, be carefully appraised. The second main objective in measuring agricultural income is to enable a comparison of changes in the well-being of those engaged in farming with changes in the well-being of those engaged in other industries. These objectives constitute the important reasons for undertaking income analysis. The measurements of individual farm returns, such as labour income, labour earnings, or variations of gross or net income, are commonly used in studying the efficiency of management in the farm business. For this purpose they are very useful, but their field of service is definitely limited.

Production and consumption activities in any economy have a
direct impact on the environment. Although increased economic activity
and population growth in developing countries continue to exert enormous
pressure on their natural environments, the role of the environment is
neglected in the estimation of national income. Such neglect at the
macroeconomic level is at least in part, an important cause of
environmental degradation in developing countries. Since the United
Nations Conference on Environment and Development in 1992 at Rio and
even as early as middle of the 1980s, a substantial literature had
developed on methods to integrate the environment into the economic
development process. The main assertion in this literature is that
natural resources represent a form of capital that is analogous to the
stock of manufactured capital. Sustainable income can be determined by
allocating a portion of income to allow for the deprecation of natural
capital [Ahmed, El Serafy, and Lutz (1989) and Solow (1992)]. Indonesia
had average real GDP growth rates of more than five percent per year up
to the early 1990s [World Bank (1994)]. But income inequality (measured
by the Gini coefficient) has been high. Although inequality continues to
be quite high, especially between rural and urban populations, Indonesia
has been successful in poverty alleviation up to mid 1990s. In 1976
almost 40 percent of its population was below the poverty line, which in
1993 decreased to less than 14 percent [Todaro (1994)]. Income
distributional consequences of economic growth would continue to be one
of the main policy issues in Indonesia. This is due to its large
population size, presence of different ethnic and religious groups,
large diversity between rural and urban groups, variety of natural
resources scattered over the country, huge distances and the effects of
a far-flung archipelago [Akita, Lukman, and Yamada (1999)].

Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual's income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.

Purpose. Examine associations between worksite physical activity promotion strategies and employees' physical activity and sedentary behaviors. Design. Cross-sectional. Setting. Seattle–King County, Washington and Baltimore, Maryland–Washington, D.C. regions. Subjects. Adults working outside the home (n = 1313). Mean age was 45 ± 10 years, 75.8% of participants were non-Hispanic white, 56% were male, and 51% had income ≥$70,000/year. Measures. Participants reported demographic characteristics and presence/absence of nine physical activity promotion environment and policy strategies in their work environment (e.g., showers, lockers, physical activity programs). A worksite physical activity promotion index was a tally of strategies. Total sedentary and moderate-to-vigorous physical activity (MVPA) min/d were objectively assessed via 7-day accelerometry. Total job-related physical activity minutes and recreational physical activity minutes were self-reported with the International Physical Activity Questionnaire. Analysis. Mixed-effects models and generalized estimating equations evaluated the association of the worksite promotion index with physical activity and sedentary behavior, adjusting for demographics. Results. A higher worksite promotion index was significantly associated with higher total sedentary behavior (β = 3.97), MVPA (β = 1.04), recreational physical activity (β = 1.1 and odds ratio = 1.39; away from work and at work, respectively) and negatively with job-related physical activity (β = .90). Conclusions. Multiple worksite physical activity promotion strategies based on environmental supports and policies may increase recreational physical activity and should be evaluated in controlled trials. These findings are particularly important given the increasingly sedentary nature of employment. (Am J Health Promot 2011;25[4]:264–271.)

The aim of this article is to analyse a specific set of support instruments for the unemployed, namely those introduced in 1986 by the bipartite French unemployment insurance fund (UNEDIC) for those in casual employment. Under the new scheme, unemployed people were able to combine a limited income from casual employment with a part of their unemployment benefit, for a period of up to 18 months. Based on the dubious assumption that even precarious employment is better than full-time unemployment, this opportunity was designed to induce the unemployed to take up employment of any kind. The article considers in detail the economic and social context prevailing prior to the introduction of these measures, concluding that precarious, casual employment far from serves as a springboard to permanent employment, but that, on the contrary, it may lead an increasing number of people into underemployment and low-pay traps.

Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual's income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.

Stratifying behaviors based on demographics and socioeconomic status is crucial for political and economic planning. Traditional methods to gather income and demographic information, like national censuses, require costly large-scale surveys both in terms of the financial and the organizational resources needed for their successful collection. In this study, we use data from social media to expose how behavioral patterns in different socioeconomic groups can be used to infer an individual's income. In particular, we look at the way people explore cities and use topics of conversation online as a means of inferring individual socioeconomic status. Privacy is preserved by using anonymized data, and abstracting human mobility and online conversation topics as aggregated high-dimensional vectors. We show that mobility and hashtag activity are good predictors of income and that the highest and lowest socioeconomic quantiles have the most differentiated behavior across groups.